pd.Series()函数解析（最清晰的解释） |
您所在的位置：网站首页 › series函数用法excel › pd.Series()函数解析（最清晰的解释） |
pd.Series()函数解析（最清晰的解释）
|
欢迎关注WX公众号：【程序员管小亮】
1. Series介绍
Pandas模块的数据结构主要有两：1、Series ；2、DataFrame series是一个一维数组，是基于NumPy的ndarray结构。Pandas会默然用0到n-1来作为series的index，但也可以自己指定index(可以把index理解为dict里面的key)。 2. Series创建 pd.Series([list]，index=[list])参数为list；index为可选参数，若不填写则默认index从0开始；若填写则index长度应该与value长度相等。 import pandas as pd s=pd.Series([1,2,3,4,5]，index=['a','b','c','f','e']) print s pd.Series({dict})以一字典结构为参数。 import pandas as pd s=pd.Series({'a':1,'b':2,'c':3,'f':4,'e':5}) print s 3. Series取值s[index] or s[[index的list]] 取值操作类似数组，当取不连续的多个值时可以以list为参数 import pandas as pd import numpy as np v = np.random.random_sample(50) s = pd.Series(v) s1 = s[[3, 13, 23, 33]] s2 = s[3:13] s3 = s[43] print("s1", s1) print("s2", s2) print("s3", s3) s1 3 0.064095 13 0.354023 23 0.225739 33 0.959288 dtype: float64 s2 3 0.064095 4 0.405651 5 0.024181 6 0.367606 7 0.844005 8 0.405313 9 0.102824 10 0.806400 11 0.950502 12 0.735310 dtype: float64 s3 0.42803253918 4. Series取头和尾的值.head(n)；.tail(n) 取出头n行或尾n行，n为可选参数，若不填默认5 import pandas as pd import numpy as np v = np.random.random_sample(50) s = pd.Series(v) print("s.head()", s.head()) print("s.head(3)", s.head(3)) print("s.tail()", s.tail()) print("s.head(3)", s.head(3)) s.head() 0 0.714136 1 0.333600 2 0.683784 3 0.044002 4 0.147745 dtype: float64 s.head(3) 0 0.714136 1 0.333600 2 0.683784 dtype: float64 s.tail() 45 0.779509 46 0.778341 47 0.331999 48 0.444811 49 0.028520 dtype: float64 s.head(3) 0 0.714136 1 0.333600 2 0.683784 dtype: float64 5. Series常用操作 import pandas as pd import numpy as np v = [10, 3, 2, 2, np.nan] v = pd.Series(v) print("len():", len(v)) # Series长度,包括NaN print("shape():", np.shape(v)) # 矩阵形状，（，） print("count():", v.count()) # Series长度，不包括NaN print("unique():", v.unique()) # 出现不重复values值 print("value_counts():\n", v.value_counts()) # 统计value值出现次数 len(): 5 shape(): (5,) count(): 4 unique(): [ 10. 3. 2. nan] value_counts(): 2.0 2 3.0 1 10.0 1 dtype: int64 6. Series加法 import pandas as pd import numpy as np v = [10, 3, 2, 2, np.nan] v = pd.Series(v) sum = v[1:3] + v[1:3] sum1 = v[1:4] + v[1:4] sum2 = v[1:3] + v[1:4] sum3 = v[:3] + v[1:] print("sum", sum) print("sum1", sum1) print("sum2", sum2) print("sum3", sum3) sum 1 6.0 2 4.0 dtype: float64 sum1 1 6.0 2 4.0 3 4.0 dtype: float64 sum2 1 6.0 2 4.0 3 NaN dtype: float64 sum3 0 NaN 1 6.0 2 4.0 3 NaN 4 NaN dtype: float64 7. Series查找 范围查找 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa[sa>19]) jim 22.0 lj 24.0 ton 20.0 Name: age, dtype: float64 中位数 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print("sa.median()", sa.median()) sa.median() 20.0 8. Series赋值 import pandas as pd import numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(s) print('----------------') sa['ton'] = 99 print(sa) {'ton': 20, 'mary': 18, 'jack': 19, 'jim': 22, 'lj': 24, 'car': None} ---------------- car NaN jack 19.0 jim 22.0 lj 24.0 mary 18.0 ton 99.0 Name: age, dtype: float64python课程推荐。 |
【本文地址】
今日新闻 |
推荐新闻 |